Analysis of Scientific Contributions to Agricultural Development and Food Security in Ecuador
Bibliographic record
Abstract
In alignment with the 2030 Sustainable Development Goals (SDGs), Ecuador's National Development Plan accentuates SDG 2's commitment to escalating agricultural productivity for food security.Notably, Ecuador contributes significantly as a producer and exporter of foods such as bananas, cocoa, and frozen vegetables, with bananas encompassing 27% of non-oil exports.The past decade has witnessed an appreciable growth in the country's scientific contributions to the agricultural sector.This study employs bibliometrics and systematic reviews, leveraging mapping techniques via bibliometrix and VOSviewer tools, to analyse 1,300 scientific documents spanning from 1973 to 2022.Within this corpus, distinct research trends materialised across three distinct eras.The period from 1973-2000 was characterised by studies on indigenous plants, ivory vegetable production, and the nutritional value of quinoa.The subsequent decade (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) witnessed an expansion into the realms of pollination, properties of Ishpingo oil, genetics, and biomass.The latest period (2011-2022) marked a shift in focus towards cocoa fermentation, genetics, soil analysis, strawberry antioxidants, and eucalyptus properties.Considering Ecuador's growing reliance on global trade integration, the need to address postharvest diseases using biocontrol agents, and the advancement of irrigation automation for water efficiency have emerged as critical areas.The assessment of the quality of scientific contributions in agriculture underscores a direct correlation between food production, the extent of the country's agricultural coverage, and the proportion of employment within the agricultural sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.069 | 0.094 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".